Using feature selection to aid an iconic search through an image database

Messer, Kieron; Kittler, Josef

doi:10.1109/icassp.1997.595322

Cited by 2 publications

(2 citation statements)

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“…First, new applications dealing with vast amounts of data have been developed, such as data mining [24], [17], multimedia information retrieval [18], [16], [15], and medical data processing [26]. Since the fast processing of a large volume of data is critical in these applications for the purpose of real-time processing or to provide a quick response to users, limiting the number of features is a very important requirement.…”

Section: Introductionmentioning

confidence: 99%

Hybrid genetic algorithms for feature selection

Lee

Moon

2004

IEEE Trans. Pattern Anal. Machine Intell.

712

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Abstract-This paper proposes a novel hybrid genetic algorithm for feature selection. Local search operations are devised and embedded in hybrid GAs to fine-tune the search. The operations are parameterized in terms of their fine-tuning power, and their effectiveness and timing requirements are analyzed and compared. The hybridization technique produces two desirable effects: a significant improvement in the final performance and the acquisition of subset-size control. The hybrid GAs showed better convergence properties compared to the classical GAs. A method of performing rigorous timing analysis was developed, in order to compare the timing requirement of the conventional and the proposed algorithms. Experiments performed with various standard data sets revealed that the proposed hybrid GA is superior to both a simple GA and sequential search algorithms.Index Terms-Feature selection, hybrid genetic algorithm, sequential search algorithm, local search operation, atomic operation, multistart algorithm.

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Section: Introductionmentioning

confidence: 99%

Hybrid genetic algorithms for feature selection

Lee

Moon

2004

IEEE Trans. Pattern Anal. Machine Intell.

712

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“…Interest in feature selection is increased due to several reasons. Reasons such as new applications dealing with vast amount of data have been developed, e.g., data mining,78,79 multimedia information retrieval,80,81 and medical data processing 82. Since the first processing of a large volume of data is critical in these applications for the purpose of real‐time processing or to provide a quick response to users, limiting the number of features is a very important requirement.…”

Section: Data Mining Using Genetic Algorithms: Attribute Selectionmentioning

confidence: 99%

Revisiting evolutionary algorithms in feature selection and nonfuzzy/fuzzy rule based classification

Dehuri

Ghosh

2013

WIREs Data Min & Knowl

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This paper discusses the relevance and possible applications of evolutionary algorithms, particularly genetic algorithms, in the domain of knowledge discovery in databases. Knowledge discovery in databases is a process of discovering knowledge along with its validity, novelty, and potentiality. Various genetic-based feature selection algorithms with their pros and cons are discussed in this article. Rule (a kind of high-level representation of knowledge) discovery from databases, posed as single and multiobjective problems is a difficult optimization problem. Here, we present a review of some of the genetic-based classification rule discovery methods based on fidelity criterion. The intractable nature of fuzzy rule mining using single and multiobjective genetic algorithms reported in the literatures is reviewed. An extensive list of relevant and useful references are given for further research. C 2013 Wiley Periodicals, Inc.

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Using feature selection to aid an iconic search through an image database

Cited by 2 publications

References 13 publications

Hybrid genetic algorithms for feature selection

Hybrid genetic algorithms for feature selection

Revisiting evolutionary algorithms in feature selection and nonfuzzy/fuzzy rule based classification

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